Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Blueberry Bioactive Properties and Their Benefits for Health: A Review

Research intended to elucidate the biochemical role of dietary antioxidant bioactive substances, such as blueberry constituents, particularly anthocyanins, associated with their functional properties, has been conducted. The purpose was to identify the effect of these substances on specific biomarkers and the biological occurrence of these correlations, by reducing the risk of developing degenerative diseases, such as diabetes mellitus, cancer, coronary heart disease, hypertension, hypercholesterolemia, and obesity. In view of the above, the aim with this literature review was to gather information about the antioxidant bioactive substances from blueberries, showing how they acted to their antioxidant power, in relation to reducing the risk of developing degenerative chronic diseases. These substances were observed as significant variations of the bioactive composition in the blueberries, influenced by soil and climate conditions. Blueberries, through the biological function of their bioactive substances, would have shown positive effects in reducing the risk of developing several non-transferable chronic diseases

Efeito da umidade e do período de armazenamento hermético na contaminação natural por fungos e a produção de micotoxinas em grãos de aveia Effects of grain moisture and hermetic storage on fungi contamination and mycotoxin production in oats

Com o presente trabalho, objetivou-se avaliar a contaminação natural por fungos, produção de micotoxinas, composição em ácidos graxos e concentração de CO2 intergranular em grãos de aveia armazenados no sistema hermético por 12 meses. Grãos de aveia, da cultivar UPF 18 foram colhidos com 16% de umidade e realizado a secagem estacionária em protótipo silo-secador até as umidades de 15%, 12% e 9%. Para os tratamentos com 18% e 21% de umidade foi realizada a reidratação pela colocação dos grãos em atmosfera saturada por cinco dias. Determinou-se a composição centesimal aproximada, composição em ácidos graxos, contaminação natural por fungos, presença de micotoxinas e concentração de CO2 intergranular. Os resultados foram analisados pela análise de variância, comparação de médias pelo teste de Tukey e análise de regressão. Não foi detectada a presença das micotoxinas aflatoxina B1, B2, G1, G2, zearalenona e ocratoxina A. A quantidade de CO2 na atmosfera intergranular apresentou comportamento linear e foi proporcional à umidade dos grãos e tempos de armazenamento. O aumento do tempo de armazenamento e da umidade dos grãos intensificou a redução de ácidos graxos insaturados, com predominância de ação sobre linoléico e linolênico.<br>This research aimed to evaluate the natural contamination by fungi, production of mycotoxins, fatty acids composition and the intergranular CO2 concentration in oat grains stored in a hermetic system for 12 months. The oat grains were harvested with 16% of moisture, being dried until 15%, 12% and 9% of moisture. Oat grains with 18% and 21% of moisture were obtained by a rehidrated, setting the grains in a saturated atmosphere for 5 days. The proximate percent composition, fatty acids profile, natural fungi contamination, mycotoxin production and the CO2 concentration were evaluated. The results were analyzed by analysis of variance and comparisons of means by Tukey's test and regression analysis of datas. The production of aflatoxins (B1, B2, G1e G2), zearalenone and ochratoxin A were not detected. The amount of CO2 in the intergranular atmosphere in the hermetic storage showed a linear behavior and was directly related to the moisture and storage time. The increasing of the storage time and grains moisture caused the reduction of unsaturated fatty acids, mainly linoleic and linolenic acids